Crime_category.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crime categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the necessary libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import squarify"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "crimes = pd.read_csv(\"/Users/oleoaraujo/Desktop/Crimes-in-Chicago/Crimes_-_2015_to_2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the dataset \n",
    "crimes = pd.read_csv('/Users/yadavvinamrata/Downloads/Crimes_-_2015_to_2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Case Number</th>\n",
       "      <th>Date</th>\n",
       "      <th>Block</th>\n",
       "      <th>Primary Type</th>\n",
       "      <th>Description</th>\n",
       "      <th>Location Description</th>\n",
       "      <th>Arrest</th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Beat</th>\n",
       "      <th>District</th>\n",
       "      <th>Ward</th>\n",
       "      <th>Community Area</th>\n",
       "      <th>Year</th>\n",
       "      <th>Updated On</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10224738</td>\n",
       "      <td>HY411648</td>\n",
       "      <td>2015-09-05 13:30:00</td>\n",
       "      <td>043XX S WOOD ST</td>\n",
       "      <td>BATTERY</td>\n",
       "      <td>DOMESTIC BATTERY SIMPLE</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>924</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2018-02-10 15:50:01</td>\n",
       "      <td>41.815117</td>\n",
       "      <td>-87.670000</td>\n",
       "      <td>(41.815117282, -87.669999562)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10224739</td>\n",
       "      <td>HY411615</td>\n",
       "      <td>2015-09-04 11:30:00</td>\n",
       "      <td>008XX N CENTRAL AVE</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>POCKET-PICKING</td>\n",
       "      <td>CTA BUS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1511</td>\n",
       "      <td>15.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2018-02-10 15:50:01</td>\n",
       "      <td>41.895080</td>\n",
       "      <td>-87.765400</td>\n",
       "      <td>(41.895080471, -87.765400451)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11646166</td>\n",
       "      <td>JC213529</td>\n",
       "      <td>2018-09-01 00:01:00</td>\n",
       "      <td>082XX S INGLESIDE AVE</td>\n",
       "      <td>THEFT</td>\n",
       "      <td>OVER $500</td>\n",
       "      <td>RESIDENCE</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>631</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019-04-06 16:04:43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10224740</td>\n",
       "      <td>HY411595</td>\n",
       "      <td>2015-09-05 12:45:00</td>\n",
       "      <td>035XX W BARRY AVE</td>\n",
       "      <td>NARCOTICS</td>\n",
       "      <td>POSS: HEROIN(BRN/TAN)</td>\n",
       "      <td>SIDEWALK</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1412</td>\n",
       "      <td>14.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2018-02-10 15:50:01</td>\n",
       "      <td>41.937406</td>\n",
       "      <td>-87.716650</td>\n",
       "      <td>(41.937405765, -87.716649687)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10224741</td>\n",
       "      <td>HY411610</td>\n",
       "      <td>2015-09-05 13:00:00</td>\n",
       "      <td>0000X N LARAMIE AVE</td>\n",
       "      <td>ASSAULT</td>\n",
       "      <td>SIMPLE</td>\n",
       "      <td>APARTMENT</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1522</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>2018-02-10 15:50:01</td>\n",
       "      <td>41.881903</td>\n",
       "      <td>-87.755121</td>\n",
       "      <td>(41.881903443, -87.755121152)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID Case Number                 Date                  Block  \\\n",
       "0  10224738    HY411648  2015-09-05 13:30:00        043XX S WOOD ST   \n",
       "1  10224739    HY411615  2015-09-04 11:30:00    008XX N CENTRAL AVE   \n",
       "2  11646166    JC213529  2018-09-01 00:01:00  082XX S INGLESIDE AVE   \n",
       "3  10224740    HY411595  2015-09-05 12:45:00      035XX W BARRY AVE   \n",
       "4  10224741    HY411610  2015-09-05 13:00:00    0000X N LARAMIE AVE   \n",
       "\n",
       "  Primary Type              Description Location Description  Arrest  \\\n",
       "0      BATTERY  DOMESTIC BATTERY SIMPLE            RESIDENCE       0   \n",
       "1        THEFT           POCKET-PICKING              CTA BUS       0   \n",
       "2        THEFT                OVER $500            RESIDENCE       0   \n",
       "3    NARCOTICS    POSS: HEROIN(BRN/TAN)             SIDEWALK       1   \n",
       "4      ASSAULT                   SIMPLE            APARTMENT       0   \n",
       "\n",
       "   Domestic  Beat  District  Ward  Community Area  Year           Updated On  \\\n",
       "0         1   924       9.0  12.0            61.0  2015  2018-02-10 15:50:01   \n",
       "1         0  1511      15.0  29.0            25.0  2015  2018-02-10 15:50:01   \n",
       "2         1   631       6.0   8.0            44.0  2018  2019-04-06 16:04:43   \n",
       "3         0  1412      14.0  35.0            21.0  2015  2018-02-10 15:50:01   \n",
       "4         1  1522      15.0  28.0            25.0  2015  2018-02-10 15:50:01   \n",
       "\n",
       "    Latitude  Longitude                       Location  \n",
       "0  41.815117 -87.670000  (41.815117282, -87.669999562)  \n",
       "1  41.895080 -87.765400  (41.895080471, -87.765400451)  \n",
       "2        NaN        NaN                            NaN  \n",
       "3  41.937406 -87.716650  (41.937405765, -87.716649687)  \n",
       "4  41.881903 -87.755121  (41.881903443, -87.755121152)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "THEFT                                352089\n",
       "BATTERY                              289200\n",
       "CRIMINAL DAMAGE                      168064\n",
       "ASSAULT                              114338\n",
       "DECEPTIVE PRACTICE                   110579\n",
       "OTHER OFFENSE                         98589\n",
       "NARCOTICS                             84853\n",
       "BURGLARY                              70576\n",
       "MOTOR VEHICLE THEFT                   61618\n",
       "ROBBERY                               59017\n",
       "CRIMINAL TRESPASS                     37414\n",
       "WEAPONS VIOLATION                     31717\n",
       "OFFENSE INVOLVING CHILDREN            13570\n",
       "PUBLIC PEACE VIOLATION                 9687\n",
       "CRIM SEXUAL ASSAULT                    6920\n",
       "INTERFERENCE WITH PUBLIC OFFICER       6837\n",
       "SEX OFFENSE                            6462\n",
       "PROSTITUTION                           4531\n",
       "HOMICIDE                               3855\n",
       "ARSON                                  2745\n",
       "CRIMINAL SEXUAL ASSAULT                2192\n",
       "LIQUOR LAW VIOLATION                   1351\n",
       "STALKING                               1148\n",
       "GAMBLING                               1058\n",
       "KIDNAPPING                             1045\n",
       "INTIMIDATION                            901\n",
       "CONCEALED CARRY LICENSE VIOLATION       653\n",
       "OBSCENITY                               382\n",
       "NON-CRIMINAL                            143\n",
       "PUBLIC INDECENCY                         68\n",
       "HUMAN TRAFFICKING                        64\n",
       "OTHER NARCOTIC VIOLATION                 35\n",
       "NON - CRIMINAL                           25\n",
       "NON-CRIMINAL (SUBJECT SPECIFIED)          6\n",
       "RITUALISM                                 1\n",
       "Name: Primary Type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimes_per_type = crimes['Primary Type'].value_counts()\n",
    "crimes_per_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing most predominant primary types of crimes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a treemap\n",
    "fig = plt.figure(figsize=(16,10))\n",
    "squarify.plot(sizes=crimes_per_type.values,\n",
    "              value=(crimes_per_type.values[:16]*100/crimes_per_type.values.sum()).round(2),\n",
    "              label=crimes_per_type.index[:16], alpha=.8,\n",
    "              text_kwargs={'fontsize':10, 'wrap':True})\n",
    "plt.title('Total Crimes per Type (in %)')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Only Domestic Crimes by Primary Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Domestic</th>\n",
       "      <th>Arrest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primary Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BATTERY</th>\n",
       "      <td>142270</td>\n",
       "      <td>29369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTHER OFFENSE</th>\n",
       "      <td>31838</td>\n",
       "      <td>3825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASSAULT</th>\n",
       "      <td>29535</td>\n",
       "      <td>4827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIMINAL DAMAGE</th>\n",
       "      <td>19847</td>\n",
       "      <td>2319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THEFT</th>\n",
       "      <td>13445</td>\n",
       "      <td>688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFENSE INVOLVING CHILDREN</th>\n",
       "      <td>7397</td>\n",
       "      <td>1094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIMINAL TRESPASS</th>\n",
       "      <td>1941</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIM SEXUAL ASSAULT</th>\n",
       "      <td>1175</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ROBBERY</th>\n",
       "      <td>1080</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DECEPTIVE PRACTICE</th>\n",
       "      <td>1042</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX OFFENSE</th>\n",
       "      <td>1000</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BURGLARY</th>\n",
       "      <td>950</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOTOR VEHICLE THEFT</th>\n",
       "      <td>787</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STALKING</th>\n",
       "      <td>529</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRIMINAL SEXUAL ASSAULT</th>\n",
       "      <td>376</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KIDNAPPING</th>\n",
       "      <td>303</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PUBLIC PEACE VIOLATION</th>\n",
       "      <td>297</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOMICIDE</th>\n",
       "      <td>242</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARSON</th>\n",
       "      <td>126</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTIMIDATION</th>\n",
       "      <td>99</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEAPONS VIOLATION</th>\n",
       "      <td>89</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OBSCENITY</th>\n",
       "      <td>85</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NARCOTICS</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INTERFERENCE WITH PUBLIC OFFICER</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NON-CRIMINAL (SUBJECT SPECIFIED)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HUMAN TRAFFICKING</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PROSTITUTION</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIQUOR LAW VIOLATION</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NON-CRIMINAL</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RITUALISM</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAMBLING</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Domestic  Arrest\n",
       "Primary Type                                      \n",
       "BATTERY                             142270   29369\n",
       "OTHER OFFENSE                        31838    3825\n",
       "ASSAULT                              29535    4827\n",
       "CRIMINAL DAMAGE                      19847    2319\n",
       "THEFT                                13445     688\n",
       "OFFENSE INVOLVING CHILDREN            7397    1094\n",
       "CRIMINAL TRESPASS                     1941     624\n",
       "CRIM SEXUAL ASSAULT                   1175     226\n",
       "ROBBERY                               1080     117\n",
       "DECEPTIVE PRACTICE                    1042      19\n",
       "SEX OFFENSE                           1000     104\n",
       "BURGLARY                               950     132\n",
       "MOTOR VEHICLE THEFT                    787      85\n",
       "STALKING                               529      55\n",
       "CRIMINAL SEXUAL ASSAULT                376      44\n",
       "KIDNAPPING                             303      20\n",
       "PUBLIC PEACE VIOLATION                 297      74\n",
       "HOMICIDE                               242     216\n",
       "ARSON                                  126      41\n",
       "INTIMIDATION                            99      15\n",
       "WEAPONS VIOLATION                       89      65\n",
       "OBSCENITY                               85      63\n",
       "NARCOTICS                               53      53\n",
       "INTERFERENCE WITH PUBLIC OFFICER        40      32\n",
       "NON-CRIMINAL (SUBJECT SPECIFIED)         5       1\n",
       "HUMAN TRAFFICKING                        5       0\n",
       "PROSTITUTION                             4       4\n",
       "LIQUOR LAW VIOLATION                     2       2\n",
       "NON-CRIMINAL                             1       1\n",
       "RITUALISM                                1       0\n",
       "GAMBLING                                 1       1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domestic_crimes_per_type = crimes[crimes[\"Domestic\"]==1].groupby('Primary Type').sum()\n",
    "domestic_crimes_per_type = domestic_crimes_per_type[[\"Domestic\", \"Arrest\"]]\n",
    "domestic_crimes_per_type.sort_values(\"Domestic\",ascending=False,inplace=True)\n",
    "domestic_crimes_per_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a treemap\n",
    "only_domestic = domestic_crimes_per_type[[\"Domestic\"]]\n",
    "\n",
    "fig = plt.figure(figsize=(16,10))\n",
    "squarify.plot(sizes=only_domestic.values,\n",
    "              value=(only_domestic.values*100/only_domestic.values.sum()).round(2)[:10],\n",
    "              label=only_domestic.index[:10], alpha=.8,\n",
    "              text_kwargs={'fontsize':10, 'wrap':True})\n",
    "plt.title('Total Domestic Crimes per Type (in %)')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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